Interpretable rumor detection in microblogs by attending to user interactions

• Main Authors: KHOO, Ling Min Serena, CHIEU, Hai Leong, QIAN, Zhong, JIANG, Jing
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Attention mechanisms; Attention model; Long distance interactions; Social media; State of the art; Structure-aware; Tree structures; User interaction; Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/5600; https://ink.library.smu.edu.sg/context/sis_research/article/6603/viewcontent/AAAI_2020a.pdf
• Institution: Singapore Management University

We address rumor detection by learning to differentiate between the community’s response to real and fake claims in microblogs. Existing state-of-the-art models are based on tree models that model conversational trees. However, in social media, a user posting a reply might be replying to the entire thread rather than to a specific user. We propose a post-level attention model (PLAN) to model long distance interactions between tweets with the multi-head attention mechanism in a transformer network. We investigated variants of this model: (1) a structure aware self-attention model (StA-PLAN) that incorporates tree structure information in the transformer network, and (2) a hierarchical token and post-level attention model (StA-HiTPLAN) that learns a sentence representation with token-level self-attention. To the best of our knowledge, we are the first to evaluate our models on two rumor detection data sets: the PHEME data set as well as the Twitter15 and Twitter16 data sets. We show that our best models outperform current state-of-the-art models for both data sets. Moreover, the attention mechanism allows us to explain rumor detection predictions at both token-level and post-level.